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Quantitative assessment of impact damage in stitched foam-filled Aluminium honeycomb Sandwich panels by experimental and machine learning methods

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POLYMER COMPOSITES 2024年第15期45卷 13663-13675页

作者： Dhanesh, E. Nagarajan, v. A. Kumar, K. P. vinod Karthik, B. Univ Coll Engn Dept Mech Engn Nagercoil 629004 Tamil Nadu India Univ Coll Engn Dept Chem Nagercoil India

Novel Stitched Foam-filled Honeycomb Sandwich (SFHS) panels have been fabricated using vacuum-assisted resin transfer molding to address the weak interfaces between the face sheets and the core in the Foam-filled Honeycomb Sandwich (FHS) panel. The SFHS panels have shown better load-bearing capacity and performance characteristics compared to FHS panel after Low-velocity Impact (LvI) tests. After the LvI test, MATLAB image processing was used to analyze the impact damage areas and failure mechanisms. In addition, machine Learning regression algorithms were employed to predict the optimal amount of energy absorbed during low-velocity impact testing of fabricated panels with a maximum impactor drop height of 700 mm. The results indicated that nylon yarn stitching significantly improved energy absorption and interfacial behavior compared to unstitched honeycomb panels. This research also revealed that SFHS1 panels with adjacent honeycomb cell stitching are more impact resistant, provide increased load carrying capacity, and are cost-effective. These panels can be utilized by modern engineers to increase economy, durability, and functionality in industrial, automotive, and construction *** Stitched Foam Filled Honeycomb Sandwich (SFHS) panels, manufactured via resin transfer molding, and eliminates weak interfaces between the face sheets and core. SFHS panels outperformed unstitched panels in load-bearing and energy absorption during low-velocity impact, as confirmed by MATLAB image analysis showing reduced damage and failure. machine learning algorithms particularly polynomial regression model predicted maximum absorption energy precisely with 99.9% accuracy, close to experimental results. SFHS panels can be used in automotive and industrial applications due to their through-thickness stitching. Process for manufacturing and testing SFHS panels. image

关键词： low-velocity impact machine learning MATLAB regression analysis sandwich panels stitching

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LeCA: In-Sensor Learned Compressive Acquisition for Efficient machine vision on the Edge 23

LeCA: In-Sensor Learned Compressive Acquisition for Efficien...

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50th Annual International Symposium on Computer Architecture (ISCA)

作者： Ma, Tianrui Boloor, Adith Jagadish Yang, Xiangxing Cao, Weidong Williams, Patrick Sun, Nan Chakrabarti, Ayan Zhang, Xuan Washington Univ St Louis MO 63110 USA Univ Texas Austin Austin TX USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400700958

With the rapid advances of deep learning-based computer vision (Cv) technology, digital images are increasingly consumed, not by humans, but by downstream Cv algorithms. However, capturing high-fidelity and high-resolution images is energy-intensive. It not only dominates the energy consumption of the sensor itself (i.e. in low-power edge devices), but also contributes to significant memory burdens and performance bottlenecks in the later storage, processing, and communication stages. In this paper, we systematically explore a new paradigm of in-sensor processing, termed "learned compressive acquisition" (LeCA). Targeting machine vision applications on the edge, the LeCA framework exploits the joint learning of a sensor autoencoder structure with the downstream Cv algorithms to effectively compress the original image into low-dimensional features with adaptive bit depth. We employ column-parallel analog-domain processing directly inside the image sensor to perform the compressive encoding of the raw image, resulting in meaningful hardware savings, and energy efficiency improvements. Evaluated within a modern machine vision processing pipeline, LeCA achieves 4x, 6x, and 8x compression ratios prior to any digital compression, with minimal accuracy loss of 0.97%, 0.98%, and 2.01% on imageNet, outperforming existing methods. Compared with the conventional full-resolution image sensor and the state-of-the-art compressive sensing sensor, our LeCA sensor is 6.3x and 2.2x more energy-efficient while reaching a 2x higher compression ratio.

关键词： CMOS image sensor image compression autoencoder

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Toward Integrating ChatGPT Into Satellite image Annotation Workflows: A Comparison of Label Quality and Costs of Human and Automated Annotators

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERvATIONS AND REMOTE SENSING 2025年 18卷 4366-4381页

作者： Beck, Jacob Kemeter, Lukas Malte Duerrbeck, Konrad Abdalla, Mohamed Hesham Ibrahim Kreuter, Frauke Ludwig Maximilians Univ Munchen Munich Ctr Machine Learning MCML Inst Informat D-80538 Munich Germany Fraunhofer Inst Integrated Circuits IIS Ctr Appl Res Supply Chain Serv D-90411 Nurnberg Germany

High-quality annotations are a critical success factor for machine learning (ML) applications. To achieve this, we have traditionally relied on human annotators, navigating the challenges of limited budgets and the varying task-specific expertise, costs, and availability. Since the emergence of large language models (LLMs), their popularity for generating automated annotations has grown, extending possibilities and complexity of designing an efficient annotation strategy. Increasingly, computer vision capabilities have been integrated into general-purpose LLMs like ChatGPT. This raises the question of how effectively LLMs can be used in satellite image annotation tasks and how they compare to traditional annotator types. This study presents a comprehensive investigation and comparison of various human and automated annotators for image classification. We evaluate the feasibility and economic competitiveness of using the ChatGPT4-v model for a complex land usage annotation task and compare it with alternative human annotators. A set of satellite images is annotated by a domain expert and 15 additional human and automated annotators, differing in expertise and costs. Our analyzes examine the annotation quality loss between the expert and other annotators. This comparison is conducted through, first, descriptive analyzes, second, fitting linear probability models, and third, comparing F1-scores. Ultimately, we simulate annotation strategies where samples are split according to an automatically assigned certainty score. Routing low-certainty images to human annotators can cut total annotation costs by over 50% with minimal impact on label quality. We discuss implications regarding the economic competitiveness of annotation strategies, prompt engineering, and the task-specificity of expertise.

关键词： Annotations Satellite images Biological system modeling Costs Chatbots Training image annotation visualization Remote sensing Data models Automated annotations ChatGPT label quality large language models (LLMs) satellite image annotation

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Scale-adaptive gesture computing: detection, tracking and recognition in controlled complex environments

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machine vision AND applications 2024年第4期35卷 75-75页

作者： Kirupakaran, Anish Monsley Laskar, Rabul Hussain Natl Inst Technol Silchar Dept ECE Cachar 788010 Assam India

Complexity intensifies when gesticulations span various scales. Traditional scale-invariant object recognition methods often falter when confronted with case-sensitive characters in the English alphabet. The literature underscores a notable gap, the absence of an open-source multi-scale un-instructional gesture database featuring a comprehensive dictionary. In response, we have created the NITS (gesture scale) database, which encompasses isolated mid-air gesticulations of ninety-five alphanumeric characters. In this research, we present a scale-centric framework that addresses three critical aspects: (1) detection of smaller gesture objects: our framework excels at detecting smaller gesture objects, such as a red color marker. (2) Removal of redundant self co-articulated strokes: we propose an effective approach to eliminate redundant self co-articulated strokes often present in gesture trajectories. (3) Scale-variant approach for recognition: to tackle the scale vs. size ambiguity in recognition, we introduce a novel scale-variant methodology. Our experimental results reveal a substantial improvement of approximately 16% compared to existing state-of-the-art recognition models for mid-air gesture recognition. These outcomes demonstrate that our proposed approach successfully emulates the perceptibility found in the human visual system, even when utilizing data from monophthalmic vision. Furthermore, our findings underscore the imperative need for comprehensive studies encompassing scale variations in gesture recognition.

关键词： Gesture computing Computer vision Deep learning Scale variance Human-computer interaction

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AI Based Automated Gym Trainer Using machine Learning 10

AI Based Automated Gym Trainer Using Machine Learning

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Cholaraja, K. Gururaj, M. Swasthika, v. Gnanasambandam, S.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'Smart Exercise Counter using Computer vision' is a groundbreaking system that blends cutting-edge computer vision technology with exercise monitoring. In an age where fitness and health are paramount, this innovative solution addresses the limitations of conventional fitness tracking methods by offering real-time, accurate feedback and comprehensive data analysis. By utilizing computer vision algorithms, this system tracks and analyzes the movements of individuals during their exercise routines, enhancing exercise counting, form assessment, and overall workout efficacy. Key components of the system include strategically positioned cameras, real-time image processing, machine learning models for exercise recognition, and biomechanically-informed form analysis. The system not only counts repetitions but also evaluates posture and technique, providing immediate feedback and corrections. Users can access their exercise data and progress through an intuitive mobile app or web dashboard, making it accessible and user-friendly. The applications of this technology extend beyond personal fitness, encompassing healthcare, sports training, and gym facilities. Healthcare professionals can employ it for rehabilitation and therapy, while athletes and coaches can refine training regimens. Gyms can enhance member experiences by offering advanced exercise monitoring. The 'Smart Exercise Counter using Computer vision' embodies the future of exercise monitoring, where technology aligns with health and fitness goals, offering a promising path towards more accurate, effective, and enjoyable workouts. This abstract encapsulates the transformative potential of computer vision in shaping the way we approach physical exercise and well-being. © 2024 IEEE.

关键词： Training Computer vision Accuracy Tracking Medical treatment machine learning Real-time systems Mobile applications Monitoring Sports

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Unsupervised neural network-based image restoration framework for pattern fidelity improvement and robust metrology

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JOURNAL OF MICRO-NANOPATTERNING MATERIALS AND METROLOGY-JM3 2023年第3期22卷

作者： Du, Zijian Pu, Lingling Wei, Paul Yuan, Rui Kim, Jeeeon Tan, Jiaoying ASML Silicon Valley San Jose CA 95131 USA

Background: Scanning electron microscope (SEM) images acquired by E-beam tools for inspection and metrology applications are usually degraded by blurring and additive noises. Blurring sources include the intrinsic point spread function of optics, lens aberration, and potential motion blur caused by the wafer stage movements during the image acquisition process. Noise sources include shot noise, quantization noise, and electronic read-out noise. image degradation caused by blurring and noise usually leads to noisy, inaccurate metrology results. For low-dosage metrology applications, metrology algorithms often fail to obtain successful measurements due to elevated levels of blurring and noise. image restoration and enhancement are necessary as preprocessing steps to obtain meaningful metrology results. Initial success was obtained by applying neural network-based framework to drastically improve image quality and metrology precision as is demonstrated in the previous ***: We aim to provide more details on the neural network model architecture, model regularization, and training dynamics to better understand the model's behavior. We also analyze the effect of image restoration on key metrology performances such as line edge roughness and mean critical dimension of the ***: Non-machine learning-based image quality enhancement methods fail to restore low-quality SEM images to a satisfactory degree. More recent convolutional neural networks and vision transformer-based, supervised deep learning models have achieved superior performance in various low-level image processing and computer vision tasks. Nevertheless, they require a huge amount of training data that contain high-quality ground truth images. Unfortunately, high-quality ground truth images for low-dosage SEM images do not exist. Instead, we use self-supervised U-Net combined with a fully connected network (FCN) to recover low-dosage images without the need for ground truth training images. The

关键词： deep learning U-Net convolutional neural networks fully connected network image restoration deblurring self-supervised model generative modeling metrology

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image Neural Network Classifier by Detail Attributes

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PATTERN RECOGNITION AND image ANALYSIS 2023年第2期33卷 179-183页

作者： Sai, S. v. Nikonov, v. S. Pacific Natl Univ Khabarovsk 680035 Russia

The paper presents the features of the development of a neural network analyzer of image detail. The neural network analyzer is a binary classifier based on the algorithm of search and identification of small image structures with different levels of detail (FDL). The developed neural network has a small number of neurons, requires a small amount of data and can be applied in embedded devices. The neural network classifier is considered in the context of neural network applications for computation optimization. Examples of using the classifier in image analysis and digital processing tasks are given.

关键词： neural network image analysis machine learning computation optimization

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Survey of automated crack detection methods for asphalt and concrete structures

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INNOvATIvE INFRASTRUCTURE SOLUTIONS 2024年第11期9卷 1-36页

作者： Khlifati, Oumaima Baba, Khadija Tayeh, Bassam A. Mohammed V Univ Mohammadia Engn Sch Civil Engn & Environm Lab LGCE Rabat Morocco Islamic Univ Gaza Fac Engn Civil Engn Dept POB 108 Gaza Strip Palestine

Crack detection in civil infrastructure, including roads, bridges, and buildings, is crucial for maintaining structural safety and functionality. Traditional manual inspection methods are time-consuming and prone to errors, highlighting the need for automated solutions. This study evaluates state-of-the-art computer vision techniques for automatically detecting cracks in both asphalt and concrete surfaces from 2013 to 2024. The study assesses the effectiveness and limitations of image processing, traditional machine learning, and deep learning methods for crack detection. A comparative analysis of commonly used models is presented, utilizing public datasets: SDNET2018, CCIC, and BCD for concrete images, and AigleRN, CFD, CRACK500, and GAPs for asphalt images. Based on the comparison results, advanced deep learning models such as YOLOv5 and U-Net have demonstrated superior performance in crack detection for both asphalt and concrete structures, significantly outperforming traditional methods. For concrete crack detection, YOLOv5l exhibited exceptional performance on the SDNET2018 dataset, achieving a precision of 97.7%, recall of 96.7%, and a mAP@.5 of 99.3%, with a rapid inference time of 1.1 ms, making it highly suitable for real-time applications. For asphalt crack detection, U-Net achieved outstanding results, particularly on the GAPs dataset, with a near-perfect precision of 99.53%, and on the CFD dataset, with a precision of 92.54% and an F1-score of 89.90%. The study also highlights public concrete and asphalt datasets, providing details on methodology, including the number of images, image sizes, and noted noise factors. Additionally, it discusses the impact of data source variability on crack detection methods, showcasing the applications, strengths, and limitations of multi-sensor fusion techniques. Finally, unresolved challenges such as imbalanced datasets, high inference times, and complex network architectures are identified, with suggestions for future

关键词： Crack detection Asphalt Concrete image processing machine learning Deep learning Public datasets Data source variability Multi-sensor fusion

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Towards stronger illumination robustness of local feature detection and description based on auxiliary learning

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SIGNAL image AND vIDEO processing 2024年第SUPPL 1期18卷 575-584页

作者： Bian, Houqin Fan, Shihe Zhang, Haolin Qin, Lunming Cui, Haoyang Wang, Xi Shanghai Univ Elect Power Coll Elect & Informat Engn Shanghai 201306 Peoples R China Xi An Jiao Tong Univ Natl Engn Res Ctr Visual Informat & Applicat Natl Key Lab Human Machine Hybrid Augmented Intell Xian Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

Local feature detection and description play a crucial role in various computer vision tasks, including image matching. variations in illumination conditions significantly affect the accuracy of these applications. However, existing methods inadequately address this issue. In this paper, a novel algorithm based on illumination auxiliary learning module (IALM) is introduced. Firstly, a new local feature extractor named illumination auxiliary Superpoint (IA-Superpoint) is established, based on the integration of IALM and Superpoint. Secondly, illumination-aware auxiliary training focuses on capturing the effects of illumination variations during feature extraction through tailored loss functions and jointly learning mechanisms. Lastly, in order to evaluate the illumination robustness of local features, a metric is proposed by simulating various illumination disturbances. Experiments on HPatches and RDNIM datasets demonstrate that the performance of local feature extraction is greatly improved by our method. Compared to the baseline method, the proposed method exhibits improvements in both mean matching accuracy and homography estimation.

关键词： Deep learning methods Local feature Auxiliary learning image matching

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MSHCCT: A Multiscale Compact Convolutional Network for High-Resolution Aerial Scene Classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Jamali, Ali Roy, Swalpa Kumar Lu, Bing Beni, Leila Hashemi Kakhani, Nafiseh Ghamisi, Pedram Simon Fraser Univ Dept Geog Burnaby BC V5A 1S6 Canada Alipurduar Govt Engn & Management Coll Dept Comp Sci & Engn Alipurduar 736206 West Bengal India North Carolina A&T State Univ Dept Built Environm Geomat Program Greensboro NC 27411 USA Univ Tubingen Fac Sci Dept Geosci Soil Sci & Geomorphol GrpCRC ResourceCultures 107 D-72070 Tubingen Germany Univ Tubingen DFG Cluster Excellence Machine Learning D-72070 Tubingen Germany Landbanking Grp GmbH D-80333 Munich Germany Helmholtz Zentrum Dresden Rossendorf HZDR D-09599 Freiberg Germany Univ Lancaster Lancaster LA1 4YR England

The growing popularity of vision transformers (viTs) in remote sensing image classification is due to their ability to effectively capture long-range dependencies. However, their high computational cost and memory footprint limit their applicability, particularly for small-scale datasets and resource-constrained environments. To address these challenges, we propose the multiscale multihead compact convolutional transformer (MSHCCT), a lightweight yet powerful model that integrates convolutional tokenization with small-scale viTs to enhance multiscale feature representation while maintaining computational efficiency. Despite a modest increase in parameters and training time, MSHCCT achieves superior classification accuracy and robustness on high-resolution aerial scenes. Importantly, our approach eliminates the need for model pretraining, additional datasets, or multisensor data fusion, ensuring a computationally efficient and practical solution for remote sensing applications. The code will be made publicly available at https://***/aj1365/MSHCCT

关键词： Transformers Computer architecture Remote sensing Feature extraction Computer vision Computational modeling Head Adaptation models Training Scene classification Attention mechanism compact convolutional network image classification vision transformers (viTs)

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